A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Git sees every file in your working copy as one of three things:

1. tracked - a file which has been previously staged or committed;
2. untracked - a file which has not been staged or committed; or
3. ignored - a file which Git has been explicitly told to ignore.

Ignored files are usually build artifacts and machine generated files that can be derived from your repository source or should otherwise not be committed. Some common examples are:

• dependency caches, such as the contents of /node_modules or /packages
• compiled code, such as .o, .pyc, and .class files

A reusable workflow for helping an AI agent evaluate a repository, inspect the available Awesome Copilot agents, skills, and instructions, and install only the assets that are useful, safe, and relevant to the repo.

This file is intended to be shared with an AI coding agent inside VS Code.

Last updated: 20251123

• SMB performance on Apple Silicon Macs (M1/M2/M3/M4) is often dramatically slower compared to Intel-based Macs or Linux/Windows PCs.
• This is an extremely common issue with other users employing Synology, TrueNAS, or Linux Samba.

Often times we want to verify that an object is fully built before using it. In many cases, this is done at runtime, in some sort of build() method. This is an implementation that verifies completeness using generics at compile time.

/*
 * A builder pattern that checks if every field is set before building at compile-time.
 * This means, no runtime checks, if it compiles, it's fully initialized. Thus, there's no
 * overhead of checking if the struct is valid.
 *
 * (It comes at the cost of being a bit verbose, though ergonomics could be improved with a

1. Make sure your MSFS24 Install on Linux is running well - have at least 1 startup done. I personally ran MSFS24 through proton-10.0-4 (the Steam version).

2. After running, verify the MSFS24 compatdata exists MSFS24 Steam App ID should be: 2537590

Run:

ls ~/.steam/steam/steamapps/compatdata/2537590